Many types of data sets determined by measuring physical events contain data points that can be characterized as having more than one dimension. Examples of such multidimensional data sets include financial transaction data sets containing data points representing price in one dimension and volume in another dimension, Internet protocol (IP) network flow data sets containing data points representing numbers of packets in one dimension and packet sizes in another dimension, etc. In addition to possessing statistical characteristics in each individual dimension, such multidimensional data sets can also possess statistical relationships between dimensions. For one-dimensional data, quantiles representing statistical rankings of the data can be intuitive, robust statistical point descriptors of the underlying one-dimensional data. However, applying such single-dimensional quantile methods to summarize a multidimensional data set typically ignores the potentially rich statistical information characterizing the inter-dimensional relationship(s) between the data points. Also, existing methods to determine multidimensional quantile descriptors typically yield algebraic curves or region-based statistical descriptors, rather than statistical point descriptors capable of being associated with particular data points in an underlying multidimensional data set.